The nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. 

Installation: online;

Dependent libraries: sklearn;

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Gradient boosting machine is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.

Installation: online;

Dependent libraries: sklearn;

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Ridge regression is a way to create a parsimonious model when thenumber of predictor variables in a set exceeds thenumber of observations, or when a data set hasmulticollinearity.

Installation: online;

Dependent libraries: sklearn;

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Naive Bayes is a simple technique for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set. There is not a single algorithm for training such classifiers, but a family of algorithms.

Installation: online;

Dependent libraries: sklearn;

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Non-Local means is an improved filtering to the traditional neighborhood filtering method. Considering the self similarity of the image, it makes full use of the redundant information in the image and can maintain the details of the image to the greatest extent while denoising.

A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples.

Installation: online;

Dependent libraries: sklearn;

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The K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible.

Installation: online;

Dependent libraries: sklearn;

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Apriori is an algorithm for frequent item set mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.

Installation: online;

Dependent libraries: sklearn;

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Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components.

Installation: online;

Dependent libraries: sklearn;

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Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual tree.

Installation: online;

Dependent libraries: sklearn;

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